I-Corps: Contextualization of Explainable Artificial Intelligence (AI) for Better Health

I-Corps项目更广泛的影响/商业潜力是开发可解释的医疗保健数据人工智能（XAI）方法。目前，电子医疗记录的数量正在增加，而机器学习和深度学习模型，特别是大型语言模型，已被用于解决医疗保健需求。然而，医疗保健领域受到高度监管，黑盒人工智能模型的可解释性对任何人工智能应用程序都变得越来越重要。用户需要理解和信任机器学习算法产生的结果和输出。提出的XAI技术可用于描述人工智能模型、其预期影响和潜在偏差。此外，拟议的技术可用于将人工智能预测转化为可解释的医疗干预措施，以实现人工智能在医疗保健领域的最后一英里交付。这些技术的商业潜力可能会影响三个主要群体：健康保险公司，他们可能会提供更好的护理管理干预措施，并实现基于XAI的个性化护理交付；健康分析公司依靠解释来进一步提高产品质量，满足政府法规要求；医疗设备初创公司需要根据从医疗设备收集的数据提供可解释的分析输出，以丰富他们的用户体验。I-Corps项目的基础是开发可解释的人工智能（XAI）方法，应用于医疗保健行业。提供可解释性对于人工智能健康应用至关重要。医疗保健是一个具有多模态数据的独特领域：关于患者人口统计信息的表格数据、关于医疗记录的文本数据、关于生命体征测量的时间序列数据、关于医疗扫描的图像以及关于脑电图和心电图的小波数据。为了提供这些数据的整体视图，使用深度学习在不同模式的数据上创建通用嵌入，并构建健康风险的预测模型。但深度学习方法缺乏透明度，需要可解释性。提出的技术将综合梯度与消融研究相结合，以确定解释中不同数据成分的影响因素。此外，该平台将知识图谱添加到预测和解释工作流程中，以检测贡献特征之间的关系，从而生成具有整体视图的解释，并将权重或特征重要性转换为风险评分，从而实现AI在医疗保健领域的最后一英里交付。提出的XAI方法可用于解释输入数据成分的重要性，确定个体患者水平和患者队列水平的贡献特征；扩展和节省计算资源；并通过使用强化学习来增强正反馈来自我提升。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of the explainable Artificial Intelligence (XAI) methods for healthcare data. Currently, the number of electronic medical records is increasing while machine learning and deep learning models, especially large language models, have been employed to address healthcare needs. However, the healthcare domain is highly regulated and explainability for the black-box AI model becomes increasingly critical for any AI application. Users need to comprehend and trust the results and output created by machine learning algorithms. The proposed XAI technology may be used to describe an AI model, its expected impact, and potential biases. Further, the proposed technology may be used to transfer AI predictions into explainable medical interventions to enable the last mile delivery of AI in healthcare The commercial potential of these technologies may impact three major groups: health insurance companies who may provide better care management interventions and achieve personalized care delivery based on XAI; health analytic companies who rely on explanation to further enhance their products and meet the government regulations; and medical device startups who demand explainable analytical outputs based on the collected data from medical devices to enrich their user experience.This I-Corps project is based on the development of explainable Artificial Intelligence (XAI) methods applied to the healthcare industry. Providing explainability is critical for AI health applications. Healthcare is a unique domain with multimodality data: tableau data about patient demographic information, textual data about medical notes, time series data about vital sign measures, images about medical scan, and wavelet data about EEG and ECG. To provide a holistic view of these data, deep learning is used to create universal embeddings on different modalities of data and build the prediction models for health risks. But deep learning methods lack transparency and demand explainability. The proposed technology combines integrated gradients with ablation studies to identify the contributing factors of different data components in the explanation. In addition, the proposed platform adds knowledge graphs into the prediction and explanation workflow to detect the relationships between contributing features to generate an explanation with a holistic view, and translates weights or feature importance into risk scores to enable the last mile delivery of AI in healthcare. The proposed XAI method may be used to explain the importance of input data components, identify the contributing features at the individual patient level and the patient cohort level; scale and save computational resources; and self-improve by using reinforcement learning to enhance positive feedback.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.